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Fast tree aggregation for consensus hierarchical clustering.

Audrey Hulot1,2,3, Julien Chiquet4, Florence Jaffrézic5

  • 1Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, 78350, France. audrey.hulot@inrae.fr.

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Summary
This summary is machine-generated.

We introduce mergeTrees, a novel method for aggregating multiple hierarchical clustering trees into a single consensus tree. This approach enhances data integration and cluster analysis, particularly for omics data, offering robust and interpretable results.

Keywords:
Consensus clusteringData integrationHierarchical clusteringOmicsUnsupervised learning

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Data integration from multiple sources is a challenge in unsupervised learning and clustering.
  • Hierarchical clustering (HC) is popular for single-source data due to interpretable tree structures.
  • Applying HC to multiple data sources presents computational and interpretation difficulties.

Purpose of the Study:

  • To develop a method for aggregating multiple hierarchical clustering trees into a consensus tree.
  • To address the computational and interpretation issues of applying HC to multi-source data.
  • To provide a robust and efficient approach for cluster analysis in omics and other fields.

Main Methods:

  • Propose mergeTrees, an exact algorithm to aggregate a set of trees with identical leaves.
  • The consensus tree construction ensures clusters at height h contain individuals consistently clustered across all input trees at that height.
  • Implementation in R/C++ allows processing of large datasets and offers a spectral variant for efficient analysis.

Main Results:

  • The mergeTrees method is computationally efficient, with proven complexity O(nq).
  • Simulations demonstrate high effectiveness in processing numerous large trees.
  • Application to real omics datasets showcases robustness and efficiency, with a spectral variant providing further benefits.

Conclusions:

  • The mergeTrees method facilitates efficient cluster analysis when combined with hierarchical clustering.
  • The approach is robust to missing clustering information and increased intra-cluster variability.
  • The R package mergeTrees is readily available for integration into existing or new data analysis pipelines.